梯级电站群短期优化调度模型与定价方案选择
|
摘要
随着电力市场化改革的发展,主要考虑负荷及资源优化分配的传统梯级电站群短期优化调度方式,已经不能满足发电企业的需求。因此,发电商需要根据市场规则,重新制订市场环境下梯级电站群短期优化调度方案,并在此基础上,从短期优化调度角度讨论梯级电站群定价方案选择中的“一站一价”还是“统一电价”问题。这些不仅能为国家节约大量资源,还能为发电企业带来巨大经济效益,具有广阔的应用前景。以前的研究关注更多的是传统短期优化调度问题的建模以及对模型的求解,而对市场机制下梯级电站群短期优化调度模型讨论较少,还未见从短期优化调度角度讨论梯级电站群定价选择问题。因此,本文将针对这两个问题进行分析讨论。内容包括:
首先,本文建立了市场环境下梯级电站群短期优化调度模型。系统地给出了梯级电站群的特点、分类、衔接关系以及基本参数。评述了几种常见优化准则,提出了梯级电站群在某个时段集合内利益最大化为目标的优化准则,该准则综合考虑了系统发电收益和存水收益,本文也给出了相应的约束条件,并讨论了调度模型中水流时滞、发电量与发电引用流量关系和梯级电站群存水价值系数三个特征参数。
其次,为了求解上述模型,本文设计了一种动态自适应蚁群算法。针对传统蚁群算法求解梯级电站群短期优化调度模型存在的问题以及算法自身的缺陷,本文提出了一种改进的动态自适应蚁群算法(DAACO),从动态调整信息素挥发系数、自适应地更新全局信息素、全局信息素更新方式、状态转移参数设置四个方面对算法进行了改进;并针对调度模型特点,本文构建了便于求解调度模型的动态自适应蚁群算法求解框架,讨论了求解过程中约束条件的处理、启发机制以及精英区的产生和更新等三个关键问题。
然后,本文对不同电价形式下梯级电站群短期优化调度进行了讨论。本文针对电力市场发展的不同阶段,存在不同电价机制的特点,分别对单一电价机制、两部制电价机制以及竞争电价机制的产生,组成结构以及优缺点进行了详细阐述,并构建了不同电价机制的统一模式;在此基础上,本文构建了统一上网电价模式下基于效益最大化的梯级电站群短期优化调度模型,并从理论上分别分析了不同电价机制梯级电站群短期优化调度的优化准则。
最后,本文运用澜沧江梯级电站群中三个连续电站的相关数据,验证了所建立的梯级电站群短期优化调度模型及求解算法的合理性,并从梯级电站群短期优化调度角度讨论了梯级电站群定价方案选择中的“一站一价”还是“统一电价”问题。本文首先选取分时电价下梯级电站群内采取“统一电价”形式的短期优化调度模型作为验证对象,验证所建立模型是合理性;其次分别运用ACS和DAACO算法对模型进行求解,并通过对比分析,验证所构建的算法对求解该问题是有效的;最后分别就单一电价机制和分时电价机制下梯级电站群内采取“统一电价”形式还是“一站一价”形式进行对比分析,得出梯级电站群内采取“统一电价”形式比“一站一价”形式更具优越性,更有利于资源的合理分配与利用。
With the development of innovation on electricity market, the traditional short-term optimization scheduling modes about loading and resource among cascaded hydroelectricity plants have not satisfied the demand of the generation enterprises. Thus, it is necessary for the generation enterprises to redesign the short-term optimization scheduling system according to the market rules and to discuss the problem of the choice of making-pricing schemes on“one price of one plant”and“one price of all plants”among the cascaded hydroelectricity plants from the short-term optimization scheduling degree.And all of these are both able to save a great deal resource for the country and bring enormous economic benefit for generation enterprises, and have the very widest application future. Some researchers have done more work in modeling and solving on the traditional short-term optimization scheduling, more considering in assigning the resources optimally. However it is less discussed to the model on the short-term optimization scheduling among cascaded hydroelectricity plants under market environment, much less to the choice of making pricing schemes on“one price of one plant”and“one price of all plants”. Thus, this paper analyzes and discusses on the two problems. The content includes:
Firstly, this paper builds the short-term optimization scheduling model among cascaded hydroelectricity plants under market environment.The characteristics, classify, joint relations and other basic parameters about cascaded hydroelectricity plants are firstly given in this paper.And secondly many traditional optimal rules are also commented.And then this paper brings forward a new optimal rule about maximizing the benefits of the generation enterprises in the considered aggregate among cascaded hydroelectricity plants,this rule includes the benefit of generating electricity and sluice.In the end, the corresponding limit conditions are given,and three characteristic parameters are discussed such as the time lag of the current, the relation of generating electricity quantity and generating electricity citation flux, the benefit coefficient of sluice.
Secondly, this paper designs a new Dynamic and Adaptive Ant Colony Optimization (DAACO) to solve the above model. Basing on the self-defective of the traditional ant colony algorithm and the existing problems of this algorithm to solve the above model,this paper designs a new Dynamic and Adaptive Ant Colony Optimization (DAACO), this algorithm is improved from four aspects such as dynamically adjusting the volatilization coefficient of information element, self–adaptively updating the whole information element, the updating manner of the whole information element and the state transfer parameter.In the end, this paper builds the frame to solve the above model by DAACO basing on the characteristic of the model,and discusses the three key problems such as the disposal of the limit conditions, the illumination mechanism and the generating and updating of the eliting area.
Then, this paper discusses the short-term optimization scheduling model among cascaded hydroelectricity plants based on different price models.At the different development phase of power market, there are the different price patterns, this paper firstly illustrates the structure and shortcoming of different price patterns detailedly, such as one-part price, two-part price and competition price.And then this paper also builds the unite pattern of the different price patterns.In the end, this paper builds the short-term optimization scheduling model among cascaded hydroelectricity plants under the unite price pattern basing on maximizing the benefits of the generation enterprises,and theoretically analyzes the optimal rules of different price patterns on the unite model.
In the end,this paper validates the model and algorithm by the correlative data of the cascaded hydroelectricity plants of LanCangJiang,and discusses the problem of the choice of making-pricing schemes on“one price of one plant”and“one price of all plants”among the cascaded hydroelectricity plants from the short-term optimization scheduling degree.Thus, this paper firstly chooses the short-term optimization scheduling model of zonal time price and“one price of all plants”as the validated objects, and validates that the model built is reasonable;This paper secondly solves the above model by ACS and DAACO algorithms partly, and it is gained that the algorithms designed in this paper is effective; In the end,this paper contrasts and analyzes the“one plant of one plant”and“one price of all plants”among cascaded hydroelectricity plants on one-part price and zonal time price partly, it is educed that the price mechanism of“one price of all plants”among cascaded hydroelectricity plants is much better than“one plant of one plant”, and it has more advantages on the distribution and using of resource in reason.
首先,本文建立了市场环境下梯级电站群短期优化调度模型。系统地给出了梯级电站群的特点、分类、衔接关系以及基本参数。评述了几种常见优化准则,提出了梯级电站群在某个时段集合内利益最大化为目标的优化准则,该准则综合考虑了系统发电收益和存水收益,本文也给出了相应的约束条件,并讨论了调度模型中水流时滞、发电量与发电引用流量关系和梯级电站群存水价值系数三个特征参数。
其次,为了求解上述模型,本文设计了一种动态自适应蚁群算法。针对传统蚁群算法求解梯级电站群短期优化调度模型存在的问题以及算法自身的缺陷,本文提出了一种改进的动态自适应蚁群算法(DAACO),从动态调整信息素挥发系数、自适应地更新全局信息素、全局信息素更新方式、状态转移参数设置四个方面对算法进行了改进;并针对调度模型特点,本文构建了便于求解调度模型的动态自适应蚁群算法求解框架,讨论了求解过程中约束条件的处理、启发机制以及精英区的产生和更新等三个关键问题。
然后,本文对不同电价形式下梯级电站群短期优化调度进行了讨论。本文针对电力市场发展的不同阶段,存在不同电价机制的特点,分别对单一电价机制、两部制电价机制以及竞争电价机制的产生,组成结构以及优缺点进行了详细阐述,并构建了不同电价机制的统一模式;在此基础上,本文构建了统一上网电价模式下基于效益最大化的梯级电站群短期优化调度模型,并从理论上分别分析了不同电价机制梯级电站群短期优化调度的优化准则。
最后,本文运用澜沧江梯级电站群中三个连续电站的相关数据,验证了所建立的梯级电站群短期优化调度模型及求解算法的合理性,并从梯级电站群短期优化调度角度讨论了梯级电站群定价方案选择中的“一站一价”还是“统一电价”问题。本文首先选取分时电价下梯级电站群内采取“统一电价”形式的短期优化调度模型作为验证对象,验证所建立模型是合理性;其次分别运用ACS和DAACO算法对模型进行求解,并通过对比分析,验证所构建的算法对求解该问题是有效的;最后分别就单一电价机制和分时电价机制下梯级电站群内采取“统一电价”形式还是“一站一价”形式进行对比分析,得出梯级电站群内采取“统一电价”形式比“一站一价”形式更具优越性,更有利于资源的合理分配与利用。
With the development of innovation on electricity market, the traditional short-term optimization scheduling modes about loading and resource among cascaded hydroelectricity plants have not satisfied the demand of the generation enterprises. Thus, it is necessary for the generation enterprises to redesign the short-term optimization scheduling system according to the market rules and to discuss the problem of the choice of making-pricing schemes on“one price of one plant”and“one price of all plants”among the cascaded hydroelectricity plants from the short-term optimization scheduling degree.And all of these are both able to save a great deal resource for the country and bring enormous economic benefit for generation enterprises, and have the very widest application future. Some researchers have done more work in modeling and solving on the traditional short-term optimization scheduling, more considering in assigning the resources optimally. However it is less discussed to the model on the short-term optimization scheduling among cascaded hydroelectricity plants under market environment, much less to the choice of making pricing schemes on“one price of one plant”and“one price of all plants”. Thus, this paper analyzes and discusses on the two problems. The content includes:
Firstly, this paper builds the short-term optimization scheduling model among cascaded hydroelectricity plants under market environment.The characteristics, classify, joint relations and other basic parameters about cascaded hydroelectricity plants are firstly given in this paper.And secondly many traditional optimal rules are also commented.And then this paper brings forward a new optimal rule about maximizing the benefits of the generation enterprises in the considered aggregate among cascaded hydroelectricity plants,this rule includes the benefit of generating electricity and sluice.In the end, the corresponding limit conditions are given,and three characteristic parameters are discussed such as the time lag of the current, the relation of generating electricity quantity and generating electricity citation flux, the benefit coefficient of sluice.
Secondly, this paper designs a new Dynamic and Adaptive Ant Colony Optimization (DAACO) to solve the above model. Basing on the self-defective of the traditional ant colony algorithm and the existing problems of this algorithm to solve the above model,this paper designs a new Dynamic and Adaptive Ant Colony Optimization (DAACO), this algorithm is improved from four aspects such as dynamically adjusting the volatilization coefficient of information element, self–adaptively updating the whole information element, the updating manner of the whole information element and the state transfer parameter.In the end, this paper builds the frame to solve the above model by DAACO basing on the characteristic of the model,and discusses the three key problems such as the disposal of the limit conditions, the illumination mechanism and the generating and updating of the eliting area.
Then, this paper discusses the short-term optimization scheduling model among cascaded hydroelectricity plants based on different price models.At the different development phase of power market, there are the different price patterns, this paper firstly illustrates the structure and shortcoming of different price patterns detailedly, such as one-part price, two-part price and competition price.And then this paper also builds the unite pattern of the different price patterns.In the end, this paper builds the short-term optimization scheduling model among cascaded hydroelectricity plants under the unite price pattern basing on maximizing the benefits of the generation enterprises,and theoretically analyzes the optimal rules of different price patterns on the unite model.
In the end,this paper validates the model and algorithm by the correlative data of the cascaded hydroelectricity plants of LanCangJiang,and discusses the problem of the choice of making-pricing schemes on“one price of one plant”and“one price of all plants”among the cascaded hydroelectricity plants from the short-term optimization scheduling degree.Thus, this paper firstly chooses the short-term optimization scheduling model of zonal time price and“one price of all plants”as the validated objects, and validates that the model built is reasonable;This paper secondly solves the above model by ACS and DAACO algorithms partly, and it is gained that the algorithms designed in this paper is effective; In the end,this paper contrasts and analyzes the“one plant of one plant”and“one price of all plants”among cascaded hydroelectricity plants on one-part price and zonal time price partly, it is educed that the price mechanism of“one price of all plants”among cascaded hydroelectricity plants is much better than“one plant of one plant”, and it has more advantages on the distribution and using of resource in reason.
引文
[1]中华人民共和国水力资源复查成果总报告《简要本》
[2]张勇传.水电站经济运行原理.北京:中国水利水电出版社,1998.28-32.
[3]言茂松.当量电价体系及相关制度设计(三)—水电上网的当量电价法.电力系统自动化,2003,24(11):14-19.
[4] F.C.Schweppe, M.C.Caramanis, R.D.Tabors, R.E.Bohn. Spot Pricing of Electricity.Kluwer Academic Publishers,1988, 254-264.
[5] Greg Paula.New application, lower costs, open system. Electrical Word, 1991, 14(7): 28-32.
[6] WesTaylor.Competition in Electricity Markets. IEEE Power Engineering Review, 1996, 20(7):11-13.
[7] R.O Connor.10 Steps to Deregulation.IEEE Spectrum, 1986, 12(5):40-43.
[8] Javier Contreras, Oscar Candiles, JoséIgnacio de la Fuente et al. A Cobweb Bidding Model for Competitive Electricity Markets.IEEE Transations on Power System, 2002, 17(1):148-153.
[9] A.M.Oliveua, A.C.G.Melo and R.C.Souza.Optimum Price Bidding Strategy in the Short-term Market of Hydro-Dominated Systems. 8" International Conference on Probabilistic Methods Applied to Power Systems, Iowa State University, Ames, Iowa, 2004, 9:12-16.
[10] A Carlos, D.Venezuelan. Restructured Electricity Market Analysis of a Dominant Firm’s Market Power. IEEE Transations on Power System, 2001, 10(5):48-63.
[11] Brian Sadden.Hydropower Development in Southern and Southeastern Asia.IEEE Power Engineering Review, 2002, 9(3):5-9.
[12] Kahn, Edward. The electricity industry in Spain. The Electricity Journal, 1999, 9(3): 46-55.
[13]于尔铿,韩放等.电力市场.北京:中国电力出版社,1998,25-54.
[14]尚金成,黄永皓,夏清.电力市场理论研究与应用.北京:中国电力出版社,2002.8, 10-23.
[15]张粒子,郑华等.区域电力市场电价机制.北京:中国电力出版社,2004,6,87-104.
[16] Zenghongtao, Lizhaohui, Biyaxiong.Hydroelectric enterprise model based on community intelligence.Systems,Man and Cybernetics,2004 IEEE International Conference on community intelligence,Beijing.2004,10(7):6137– 6142.
[17] SO.Orero and M.R.Irving. A genetic algorithm modelling framework and solution technique for short term optimal hydrothermal scheduling. IEEE Trans.on Power Systems, 1998, 5(2):1254-1265.
[18] Naccarino J, Cheung R, Briggs W, et al .Real-Time Monitoring, Optimization and Control of a Hydroelectric Complex. IEEE Trans. on Power Systems, 1988,3(4):1769-1783.
[19] Lin Feng. A Parametric Iteration Hethod of Stochastic Dynamic Programming for Optimal Dispatch of Hydroelectric Plants. IEE 2nd International Conference on Advances in Power System Control, Operation and Management, Hong Kong, 1993, 12:1304-1324.
[20] M Piekutowski, T Litwinowicz, R.J Frowd. Optimal short-term scheduling for a large-scale cascaded hydro system. IEEE Trans. on Power Systems, 1994, 5(2): 805– 811.
[21] Jiang Chuanwen, Etorre Bompard. A self-adaptive chaotic particle swarm algorithm for short term hydroelectric system scheduling in deregulated environment. Energy Conversion and Management, 2005,46:2689–2696.
[22] Olav Bjarte Fosso,Michael Martin Belsnes. Short-term hydro scheduling in a liberalized power system. Power System Technology, 2004,11(2):1321– 1326.
[23] A.J Conejo, J.M Arroyo,J Contreras,et al.Self-scheduling of a hydro producer in a pool-based electricity market.IEEE Trans.on Power Systems,2002,11(4): 1265-1272.
[24]刘治理,马光文,岳耀峰.电力市场下的梯级水电场短期预发电计划研究.继电器.2006,13(4):46-48.
[25]李承军,余昕卉,程淑等.分时电价下水电站群短期优化调度模型研究.水利水电技术,2005,7(3):116-119.
[26]杨东方,马光文,过夏明等.竞价上网环境下梯级水电厂的经济运行问题研究.东北水利水电,2003,11(1):54-60.
[27]陈建春,马光文,王黎等.梯级水电厂日竞价优化运行方式研究.水力发电学报,2004,8(2):25-30.
[28]徐刚,马光文,涂扬举.蚁群算法求解梯级水电厂日竞价优化调度问题.水利学报,2005,8:56-61.
[29]陈洋波,胡嘉琪.隔河岩和高坝洲梯级水电站水库联合调度方案研究.水利学报,2004,3: 47-52.
[30] Villasanti, C.M.Lucken, C.Baran. Dispatch of hydroelectric generating units using multiobjective evolutionary algorithms.Transmission and Distribution Conference and Exposition:Latin America,IEEE/PES.2004,11:929-934.
[31] Garcia-Gonzalez, J.Castro. Short-term hydro scheduling with cascaded and head-dependent reservoirs based on mixed-integer linear programming.Power Tech Proceedings, 2001 IEEE Porto 2001,9(3):1024-1032.
[32] Ramos, A.T.Santos, J.R.Exposito. Short-term hydro-thermal coordination based on interior point nonlinear programming and genetic algorithms. Power Tech Proceedings, 2001 IEEE Porto 2001,9(3):986-991.
[33] Christian Lyra, M. Ferreira.A Multiobjective approach to the short-term scheduling of a hydroelectric power system.IEEE Trans.on Power Systems, 1995,11(4):1123-1129.
[34]梅亚东,朱教新.黄河上游梯级水电站短期优化调度模型及迭代解法.水力发电学报,2000,9(2):32-39.
[35] Patricia Teixeira Leite, Adriano Alber de Fran?a Mendes Carneiro.Energetic Operation Planning Using Genetic Algorithms.IEEE Trans.on Power Systems, 2002,2(1):1325-1339.
[36] R.Naresh and J.Sharma. Hydro System Scheduling Using ANN Approach. IEEE Trans.on Power Systems, 2002,2(14): 388-395.
[37] A.H.Mantawy, S.A.Soliman, M.E.El-Hawary. A new tabu search algorithm for the long-term hydro scheduling problem.Power Engineering 2002 Large Engineering Systems Conference on, LESCOPE 02. 2002,6(02):29– 34.
[38]李崇浩,纪昌明,缪益平.基于微粒群算法的梯级水电厂短期优化调度研究.水力发电学报.2006.4:94-98.
[39]袁晓辉,袁艳斌,权先璋等.基于混沌进化算法的梯级水电系统短期发电计划.电力系统自动化.2001.8:32-38.
[40]徐刚,马光文,梁武湖等.蚁群算法在水库优化调度中的应用.水科学进展,2005,16(3):397-400.
[41] Yuanxiaohui, Yuanyanbin, Zhangyongchuan.A hybrid chaotic genetic algorithm for short-term hydro system scheduling.Mathematics and Computers in Simulation.2002, 5(9):319–327.
[42] Liang Ruey-Hsun, Hsu Yuan-Yih. A hybrid artificial neural network-differential dynamic programming approach for short-term hydro scheduling.Electric Power Systems Research ,1995,33(2):77-86.
[43]李义,李承军,周建中.POA2D-PSA混合算法在短期优化调度中的应用.水电能源科学.2004.3:37-39.
[44] K Thomas, A Garth and K Ziad.A Practical Hydro, Dynamic Unit Commitment and Loading Model.IEEE Trans.on Power Systems,2001,5(16):301-306.
[45] Nilsson and D.Sjelvgren. Variable splitting applied to modeling of start-up costs in short term hydro generation scheduling.IEEE Trans.on Power Syst,1997,5(12):770-775.
[46] M Dorigo,V.Maniezzo and A.Colorni.The Ant System: Optimization by a colony of cooperating agents.IEEE Transactions on Systems, Man, and Cybernetics-Part B,1996,26(1):29-41.
[47]李士勇等.蚁群算法及其应用.哈尔滨:哈尔滨工业大学出版社,2004:45-98.
[48] M Dorigo, V Maniezzo, A Colorni, Positive feedback as a search strategy. Technical Report 91-016, Dipartimento di Elettronica, Politecnicodi Milano, Italy, 1991.
[49] M Dorigo. Optimization, learning and natural algorithms. PhD thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy, 1992 [in Italian].
[50] L M Gambardella and M Dorigo. Ant-Q:A reinforcement learning approach to the traveling salesman problem. In Priedities A and Russell S,editors.Proceedings of the Twelfth International Conference on Machine Learning(ML-95).CA:Morgan Kaufmann Publishers,1995.252-260.
[51] M Dorigo, L M Gambardella.A study of some properties of Ant-Q.Proceedings of the 4th International Conference on Parallel Problem Solving from Nature,1996, 656-665.
[52] M Dorigo and L M Gambardella. Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transaction on Evolutionary Computation,1997,1(1):53-66.
[53] T Stutale and H Hoos. Improvements on the ant system: Introducing MAX-MIN ant system. In Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, Springer Verlag,Wien,1997.245-249.
[54] T Stutale and H Hoos.MAX-MIN ant system and local search for the traveling salesman problem.Proceedings of the 1997 IEEE International Conference on Evolutionary Computation.1997,309-314.
[55] S Thomas, H H Holger. MAX-MIN ant system. Future Generation Computer System,2000,16(8):889-914.
[56] B Bullnheimer, R F Hartl and C Strauss. A new rank-based version of the Ant System: A computational study. Central European Journal foe Operations Research and Economics,1999,7(1):25-38.
[57] M Dorigo and G Di Caro.The Ant Colony Optimization meta-heuristic.In Corne D,Dorigo M and Glover F, New Ideas in Optimization.London:McGraw Hill,1999,11-32.
[58] M Dorigo, G Di Caro, LM Gambardella. Ant algorithms for discrete optimization. Artificial Life 1999,5(2):137–172.
[59] W J Gutjahr. A graph-based ant system and its convergence. Future Generat Comput Syst 2000,16(9):873–880.
[60] W J Gutjahr.A generalized convergence result for the graph based ant system.Technical Report99-09, Dept. of Statistics and Decision Support System.University of Vienna,Austria,1999.
[61] T Stützle, Dorigo M. A short convergence proof for a class of ACO algorithms. IEEE Trans Evolutionary Comput 2002;6(4):358–65.
[62] W J Gutjahr.ACO algorithms with guaranteed convergence to the optimal solution. Information Processing Letters.2002,82(3): 145-153.
[63] J H Yoo, R J La, A M Makowski.Convergence results for ant routing.Technical Reprot CSHCN 2003-46,Institute for Systems Research,University of Maryland,College Park(MD),2003.
[64] J H Yoo, R J La, A M Makowski.Convergence results for ant routing algorithms results for simple parallel network and perpectives.Technical Reprot CSHCN 2003-44,Institute for Systems Research,University of Maryland,College Park(MD),2003.
[65] A Badr, A Fahmy.A proof of convergence for ant algorithms.Intenational Journal of Intelligent Computing and Information, 2003.3(1):22-32.
[66] Y H Hou, Y W Wu, L J Lu. Generalized ant colony optimization for economic dispatch of power systems. Proceedings of the 2002 International Conference on Power System Technoligy, 2002, 1:225-229.
[67]段海滨,王道波.蚁群算法的全局收敛性研究及改进.系统工程与电子技术.2004, 26 (10): 1506-1509.
[68]冯远静,冯祖仁,彭勤科.一类自适应蚁群算法及其收敛性分析.控制理论与应用.2005, 22 (5): 713-717.
[69]石立宝,郝晋.随机摄动蚁群算法的收敛性及其数值特性分析.系统仿真学报.2004.16 (11): 2421-2424.
[70] M Zlochin, M Birattari, N Meuleau et al. Model-based search for combinatorial optimization: A critical survey. Ann Oper Res2004.131 (1–4):373–95.
[71] N Meuleau, M Dorigo. Ant colony optimization and stochastic gradient descent. Artificial Life 2002,8(2):103–21.
[72] C Blum.Theoretical and practical aspects of Ant colony optimization. Dissertations in Artificial Intelligence. Berlin: Akademische VerlagsgesellschaftAka GmbH; 2004.
[73] M Dorigo,L M Gambardella.Ant colonies for the traveling salesman problem.Biosystems.1997,(43): 73-81.
[74] V Maniezzo, A Colorni. The Ant System applied to the quadratic assignment problem. IEEE Trans Data Knowledge Engorged 1999.11(5):769–780.
[75] D Merkle, Middendorf, H Schmeck.Ant colony optimization for resource-constrained project scheduling. IEEE Trans Evolutionary Comput.2002.6(4):333–46.
[76] D Costa, A Hertz. Ants can color graphs. J Oper Res Soc 1997,9(8):295–305.
[77] LM Gambardella,éD Taillard, G Agazzi. MACS-VRPTW: A multiple ant colony system for vehicle routing problems with time windows.In: Corne D, Dorigo M, Glover F, editors. New ideas in optimization. London: McGraw-Hill; 1999.7(4): 63–76.
[78] S Alupoaei, S Katkoori. Ant colony system application to marcocell overlap removal. IEEE Trans Very Large Scale Integr (VLSI) Syst 2004,12(10):18–22.
[79] V Maniezzo, M Boschetti, M Jelasity. An ant approach to membership overlay design. In: Dorigo M, Birattari M, Blum C, Gambardella LM,Mondada F, Stützle T, editors. Proceedings of ANTS 2004—Fourth international workshop on Ant colony optimization and swarm intelligence.Lecture Notes in Comput Sci, vol. 3172. Berlin: Springer; 2004: 37–48.
[80] K Doerner, W J Gutjahr, R F Hartl et al. Pareto ant colony optimization: A metaheuristic approach to multiobjective portfolioselection. Ann Oper Res 2004,131:79–99.
[81] M Guntsch, M Middendorf. Pheromone modification strategies for ant algorithms applied to dynamic TSP. In: Boers EJW, Gottlieb J, Lanzi PL, Smith RE, Cagnoni S, Hart E, Raidl GR, Tijink H, editors. Applications of evolutionary computing: Proceedings of EvoWorkshops,2001. Lecture Notes in Comput Sci, vol. 2037. Berlin: Springer; 2001: 213–220.
[82] K Socha, M Sampels, M Manfrin. Ant algorithms for the university course timetabling problem with regard to the state-of-the-art. In:Cagnoni S, Romero Cardalda JJ, Corne DW, Gottlieb J, Guillot A, Hart E, Johnson CG, Marchiori E, Meyer A, Middendorf M, Raidl GR, editors.Applications of evolutionary computing, proceedings of EvoWorkshops 2003. Lecture Notes in Comput Sci, vol. 2611. Berlin: Springer;2003:334–345.
[83] X Gandibleux, X Delorme, V T’Kindt. An ant colony optimisation algorithm for the set packing problem. In: Dorigo M, Birattari M, Blum C,Gambardella LM, Mondada F, Stützle T, editors. Proceedings of ANTS 2004—Fourth international workshop on Ant colony optimization and swarm intelligence. Lecture Notes in Comput Sci, vol. 3172. Berlin: Springer; 2004:49–60.
[84] R Michel, M Middendorf. An island model based ant system with look ahead for the shortest supers equence problem. In: Eiben AE, B?ck T,SchoenauerM, Schwefel H-P, editors. Proceedings of PPSN-V, fifth international conference on parallel problem solving from nature. Lecture Notes in Comput Sci, vol. 1498. Berlin: Springer.1998:692–701.
[85] L M Gambardella, M Dorigo. Ant Colony System hybridized with a new local search for the sequential ordering problem. INFORMS J Comput2000,12(3):237–255.
[86] C Solnon. Ant can solve constraint satisfaction problems. IEEE Trans Evolutionary Comput 2002,6(4):347–357.
[87] R S Parpinelli, H S Lopes, A A Freitas. Data mining with an ant colony optimization algorithm. IEEE Trans Evolutionary Comput,2002,6(4):321–329.
[88] T N Bui, J R Rizzo.Finding maximum cliques with distributed ants. In: Deb K, et al., editors. Proceedings of the genetic and evolutionarycomputation conference (GECCO 2004). Lecture Notes in Comput Sci, vol. 3102. Berlin: Springer.2004:24–35.
[89] M Blesa, C Blum. Ant colony optimization for the maximum edge-disjoint paths problem. In: Raidl GR, Cagnoni S, Branke J, Corne DW,Drechsler R, Jin Y, Johnson CG, Machado P, Marchiori E, Rothlauf R, Smith GD, Squillero G, editors. Applications of evolutionary computing,proceedings of EvoWorkshops 2004. Lecture Notes in Comput Sci, vol. 3005. Berlin: Springer.2004:160–169.
[90] A Shmygelska, R Aguirre-Hernández, H H Hoos. An ant colony optimization algorithm for the 2D HP protein folding problem. In: Dorigo M,Di Caro G, Sampels M, editors. Ant algorithms—Proceedings of ANTS 2002—Third internationalworkshop. Lecture Notes in Comput Sci,vol. 2463. Berlin: Springe.2002:40–52.
[91] J D Moss, C G Johnson. An ant colony algorithm for multiple sequence alignment in bioinformatics. In: Pearson DW, Steele NC, Albrecht RF,editors. Artificial neural networks and genetic algorithms. Berlin: Springer.2003:182–186.
[92] J Bautista, J Pereira. Ant algorithms for assembly line balancing. In: Dorigo M, Di Caro G, Sampels M, editors. Ant algorithms—Proceedingsof ANTS 2002—Third international workshop. Lecture Notes in Comput Sci, vol. 2463. Berlin: Springer.2002: 65–75.
[93] L Bianchi, L M Gambardella, M Dorigo. An ant colony optimization approach to the probabilistic traveling salesman problem. In: Merelo JJ,Adamidis P, Beyer H-G, Fernández-Villacanas J-L, Schwefel H-P, editors. Proceedings of PPSN-VII, seventh international conference onparallel problem solving from nature. Lecture Notes in Comput Sci, vol. 2439. Berlin: Springer.2002:883–92.
[94]张高峰.梯级水电系统短期优化调度与自动发电控制研究:博士学位论文.华中科技大学图书馆,2004.
[95]陈森林.水库(群)调度基本理论及自动化系统研究:博士学位论文.武汉大学图书馆.2001.
[96]裴哲义,韩放.水电与电力市场.水电自动化与大坝监测.2002,26(1):24-31.
[97]曹生荣.基于知识工程和Agent理论的水库调度系统研究:博士学位论文.武汉大学图书馆.2004.
[98]刘攀.水库洪水资源化调度关键技术研究:博士学位论文.武汉大学图书馆.2004.
[99]郭生练.水库调度综合自动化系统,武汉:武汉水利电力大学出版社,2000.
[100]曾勇红,姜铁兵,张勇传.基于线性规划的梯级水电系统短期发电计划.水电自动化与大坝监测;2004,8:13-21.
[101]李建华.水电站短期优化调度研究与应用:硕士学位论文.武汉大学图书馆.2005.
[102] Mwangi Mbuthia, Hons,C.Eng. Hydrelectric system modeling for cascaded reservoir-type power stations in the Lower Tana River(seven forks scheme)in KENYA. AFRICON '92 Proceedings.3rd AFRICON Conference,1992,9(22-24):413– 416.
[103]张勇传著.水电能优化运行.湖北.华中工学院出版社.1986.6-7.
[104]刘鑫卿,张士军.梯级水电站流量滞后性影响研究.华中理工大学学报.1999,5 :74-81.
[105]胡浩.水资源价值与价格研究综述.石家庄经济学院学报,2003,26(3):229- 232.
[106]吴世勇,马光文,杨道辉.基于水值的水电商报价策略研究.水力发电.2006,32(7):74-76.
[107]吴世勇,徐温安,柳伟.在电力系统长期优化调度中的新概念—水值.水能技术经济, 1997,17(1): 108-110.
[108]黄席樾等.现代智能算法理论及应用.北京:科学出版社.2005.
[109] J L Deneubourg, J M Pasteels, J C Verhaeghe. Probabilistic behaviors in Ants: a Strategy of Errors. Journal of Theoretical Biology,1983,10(5):259-271.
[110] J L Deneubourg, S Goss. Collective patterns and decision-making. Ethology, Ecology&Evolution.1989,1:295-311.
[111] J L Deneubourg, S Aron, S Goss et al. The self-organizing exploratory pattern of the argentine ant. Journal of Insect Behaviour,1990,3:159-168.
[112] S Goss, S Aron, J L Deneubourg et al. Self-organized shortcuts in the Argentine ant. Naturwissenschaften,1989,76:579-581.
[113]陈森林.水电系统短期优化调度的一般性准则—基本概念与数学模型,武汉水力电力大学学报.1999,32(3):34-37.
[114] Jorge Valenzuela, Mainak Mazumdar. A Probability Model for the Electricity Price Duration Curve Under an Oligopoly Market.IEEE Trans.Power Syst.2005,20(8):1250-1256.
[115] B.Gjelsvik,A.Grundt,A.Karesen et al. Optimisation of hydropower operation in a liberalised market with focus on price modeling. Power Tech Proceedings, 2001 IEEE Porto Publication Date: 2001,10(9):654-668.
[116] Javier Contreras, Rosario Espínola, Francisco J. Nogales et al. ARIMA models to predict next-day electricity prices. IEEE Trans on Power System. 2003,18(3):1014-1021.
[117] J A Conejo, A P Miguel, Rosa Espínola et al. Day-ahead electricity price forecasting using the wavelet transform and ARIMA models. IEEE Trans on Power System. 2003,20(5):1035-1042.
[118]国家电力公司动力经济研究中心.水电上网电价研究.贵州水力发电.1999. 12:61-64.
[119]吴东平,杨晓谨.水电上网模式的讨论.水电自动化与大坝监测.2003, 23(6):7-8,16.
[120]顾伟忠,刘兰.我国水电上网电价的定价研究.北京机械工业学院学报,2004,19(2) :60-66.
[121]王黎,马光文.两部制上网电价定价方法初探.四川大学学报(工程科学版).2000,32(1):62-66.
[122]苏斌.采用“两部制”电价机制促进水电开发利用.中国物价.2004.04:5-10.
[123]江岳春,姚建刚,周丽兰等.基于“容量合同+效率置换+实时市场”模式的发电竞价系统的研究.电工技术学报.2006,21(1):52-57.
[124]包颖.浅议水电两部制上网电价.四川水利.2006.1:32-39.
[125]王锡凡,耿建.分段竞价与分时竞价的比较.电力系统自动化,2003,27(7):42-48.
[126]马光文,王黎,过夏明.分时电价下多用途水库的运用方式研究.水科学进展.2002, 13(5):36-41.
[127] L M Gambardella, M Dorigo. Solving symmetric and asymmetric TSPs by ant colonies. Evolutionary Computation, 1996, Proceedings of IEEE International Conference, 1996:622-627.
[128] M Dorigo, L M Gambardella. Ant colony system: a cooperative learning approach to the traveling salesman problem.IEEE Transactions on Evolutionary Computation, 1997, 1(1):53-66.
[129]詹士昌,徐婕,吴俊.蚁群算法中有关算法参数的最优选择.科技通报,2003,19(5):381-386.
[130]叶志伟,郑肇葆.蚁群算法中参数α、β、ρ设置的研究——以TSP问题为例.武汉大学学报·信息科学版,2004,29(7):597-601.
[131]华振兴.梯级水电站群短期优化调度:硕士学位论文,华中科技大学,2006.
[2]张勇传.水电站经济运行原理.北京:中国水利水电出版社,1998.28-32.
[3]言茂松.当量电价体系及相关制度设计(三)—水电上网的当量电价法.电力系统自动化,2003,24(11):14-19.
[4] F.C.Schweppe, M.C.Caramanis, R.D.Tabors, R.E.Bohn. Spot Pricing of Electricity.Kluwer Academic Publishers,1988, 254-264.
[5] Greg Paula.New application, lower costs, open system. Electrical Word, 1991, 14(7): 28-32.
[6] WesTaylor.Competition in Electricity Markets. IEEE Power Engineering Review, 1996, 20(7):11-13.
[7] R.O Connor.10 Steps to Deregulation.IEEE Spectrum, 1986, 12(5):40-43.
[8] Javier Contreras, Oscar Candiles, JoséIgnacio de la Fuente et al. A Cobweb Bidding Model for Competitive Electricity Markets.IEEE Transations on Power System, 2002, 17(1):148-153.
[9] A.M.Oliveua, A.C.G.Melo and R.C.Souza.Optimum Price Bidding Strategy in the Short-term Market of Hydro-Dominated Systems. 8" International Conference on Probabilistic Methods Applied to Power Systems, Iowa State University, Ames, Iowa, 2004, 9:12-16.
[10] A Carlos, D.Venezuelan. Restructured Electricity Market Analysis of a Dominant Firm’s Market Power. IEEE Transations on Power System, 2001, 10(5):48-63.
[11] Brian Sadden.Hydropower Development in Southern and Southeastern Asia.IEEE Power Engineering Review, 2002, 9(3):5-9.
[12] Kahn, Edward. The electricity industry in Spain. The Electricity Journal, 1999, 9(3): 46-55.
[13]于尔铿,韩放等.电力市场.北京:中国电力出版社,1998,25-54.
[14]尚金成,黄永皓,夏清.电力市场理论研究与应用.北京:中国电力出版社,2002.8, 10-23.
[15]张粒子,郑华等.区域电力市场电价机制.北京:中国电力出版社,2004,6,87-104.
[16] Zenghongtao, Lizhaohui, Biyaxiong.Hydroelectric enterprise model based on community intelligence.Systems,Man and Cybernetics,2004 IEEE International Conference on community intelligence,Beijing.2004,10(7):6137– 6142.
[17] SO.Orero and M.R.Irving. A genetic algorithm modelling framework and solution technique for short term optimal hydrothermal scheduling. IEEE Trans.on Power Systems, 1998, 5(2):1254-1265.
[18] Naccarino J, Cheung R, Briggs W, et al .Real-Time Monitoring, Optimization and Control of a Hydroelectric Complex. IEEE Trans. on Power Systems, 1988,3(4):1769-1783.
[19] Lin Feng. A Parametric Iteration Hethod of Stochastic Dynamic Programming for Optimal Dispatch of Hydroelectric Plants. IEE 2nd International Conference on Advances in Power System Control, Operation and Management, Hong Kong, 1993, 12:1304-1324.
[20] M Piekutowski, T Litwinowicz, R.J Frowd. Optimal short-term scheduling for a large-scale cascaded hydro system. IEEE Trans. on Power Systems, 1994, 5(2): 805– 811.
[21] Jiang Chuanwen, Etorre Bompard. A self-adaptive chaotic particle swarm algorithm for short term hydroelectric system scheduling in deregulated environment. Energy Conversion and Management, 2005,46:2689–2696.
[22] Olav Bjarte Fosso,Michael Martin Belsnes. Short-term hydro scheduling in a liberalized power system. Power System Technology, 2004,11(2):1321– 1326.
[23] A.J Conejo, J.M Arroyo,J Contreras,et al.Self-scheduling of a hydro producer in a pool-based electricity market.IEEE Trans.on Power Systems,2002,11(4): 1265-1272.
[24]刘治理,马光文,岳耀峰.电力市场下的梯级水电场短期预发电计划研究.继电器.2006,13(4):46-48.
[25]李承军,余昕卉,程淑等.分时电价下水电站群短期优化调度模型研究.水利水电技术,2005,7(3):116-119.
[26]杨东方,马光文,过夏明等.竞价上网环境下梯级水电厂的经济运行问题研究.东北水利水电,2003,11(1):54-60.
[27]陈建春,马光文,王黎等.梯级水电厂日竞价优化运行方式研究.水力发电学报,2004,8(2):25-30.
[28]徐刚,马光文,涂扬举.蚁群算法求解梯级水电厂日竞价优化调度问题.水利学报,2005,8:56-61.
[29]陈洋波,胡嘉琪.隔河岩和高坝洲梯级水电站水库联合调度方案研究.水利学报,2004,3: 47-52.
[30] Villasanti, C.M.Lucken, C.Baran. Dispatch of hydroelectric generating units using multiobjective evolutionary algorithms.Transmission and Distribution Conference and Exposition:Latin America,IEEE/PES.2004,11:929-934.
[31] Garcia-Gonzalez, J.Castro. Short-term hydro scheduling with cascaded and head-dependent reservoirs based on mixed-integer linear programming.Power Tech Proceedings, 2001 IEEE Porto 2001,9(3):1024-1032.
[32] Ramos, A.T.Santos, J.R.Exposito. Short-term hydro-thermal coordination based on interior point nonlinear programming and genetic algorithms. Power Tech Proceedings, 2001 IEEE Porto 2001,9(3):986-991.
[33] Christian Lyra, M. Ferreira.A Multiobjective approach to the short-term scheduling of a hydroelectric power system.IEEE Trans.on Power Systems, 1995,11(4):1123-1129.
[34]梅亚东,朱教新.黄河上游梯级水电站短期优化调度模型及迭代解法.水力发电学报,2000,9(2):32-39.
[35] Patricia Teixeira Leite, Adriano Alber de Fran?a Mendes Carneiro.Energetic Operation Planning Using Genetic Algorithms.IEEE Trans.on Power Systems, 2002,2(1):1325-1339.
[36] R.Naresh and J.Sharma. Hydro System Scheduling Using ANN Approach. IEEE Trans.on Power Systems, 2002,2(14): 388-395.
[37] A.H.Mantawy, S.A.Soliman, M.E.El-Hawary. A new tabu search algorithm for the long-term hydro scheduling problem.Power Engineering 2002 Large Engineering Systems Conference on, LESCOPE 02. 2002,6(02):29– 34.
[38]李崇浩,纪昌明,缪益平.基于微粒群算法的梯级水电厂短期优化调度研究.水力发电学报.2006.4:94-98.
[39]袁晓辉,袁艳斌,权先璋等.基于混沌进化算法的梯级水电系统短期发电计划.电力系统自动化.2001.8:32-38.
[40]徐刚,马光文,梁武湖等.蚁群算法在水库优化调度中的应用.水科学进展,2005,16(3):397-400.
[41] Yuanxiaohui, Yuanyanbin, Zhangyongchuan.A hybrid chaotic genetic algorithm for short-term hydro system scheduling.Mathematics and Computers in Simulation.2002, 5(9):319–327.
[42] Liang Ruey-Hsun, Hsu Yuan-Yih. A hybrid artificial neural network-differential dynamic programming approach for short-term hydro scheduling.Electric Power Systems Research ,1995,33(2):77-86.
[43]李义,李承军,周建中.POA2D-PSA混合算法在短期优化调度中的应用.水电能源科学.2004.3:37-39.
[44] K Thomas, A Garth and K Ziad.A Practical Hydro, Dynamic Unit Commitment and Loading Model.IEEE Trans.on Power Systems,2001,5(16):301-306.
[45] Nilsson and D.Sjelvgren. Variable splitting applied to modeling of start-up costs in short term hydro generation scheduling.IEEE Trans.on Power Syst,1997,5(12):770-775.
[46] M Dorigo,V.Maniezzo and A.Colorni.The Ant System: Optimization by a colony of cooperating agents.IEEE Transactions on Systems, Man, and Cybernetics-Part B,1996,26(1):29-41.
[47]李士勇等.蚁群算法及其应用.哈尔滨:哈尔滨工业大学出版社,2004:45-98.
[48] M Dorigo, V Maniezzo, A Colorni, Positive feedback as a search strategy. Technical Report 91-016, Dipartimento di Elettronica, Politecnicodi Milano, Italy, 1991.
[49] M Dorigo. Optimization, learning and natural algorithms. PhD thesis, Dipartimento di Elettronica, Politecnico di Milano, Italy, 1992 [in Italian].
[50] L M Gambardella and M Dorigo. Ant-Q:A reinforcement learning approach to the traveling salesman problem. In Priedities A and Russell S,editors.Proceedings of the Twelfth International Conference on Machine Learning(ML-95).CA:Morgan Kaufmann Publishers,1995.252-260.
[51] M Dorigo, L M Gambardella.A study of some properties of Ant-Q.Proceedings of the 4th International Conference on Parallel Problem Solving from Nature,1996, 656-665.
[52] M Dorigo and L M Gambardella. Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transaction on Evolutionary Computation,1997,1(1):53-66.
[53] T Stutale and H Hoos. Improvements on the ant system: Introducing MAX-MIN ant system. In Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, Springer Verlag,Wien,1997.245-249.
[54] T Stutale and H Hoos.MAX-MIN ant system and local search for the traveling salesman problem.Proceedings of the 1997 IEEE International Conference on Evolutionary Computation.1997,309-314.
[55] S Thomas, H H Holger. MAX-MIN ant system. Future Generation Computer System,2000,16(8):889-914.
[56] B Bullnheimer, R F Hartl and C Strauss. A new rank-based version of the Ant System: A computational study. Central European Journal foe Operations Research and Economics,1999,7(1):25-38.
[57] M Dorigo and G Di Caro.The Ant Colony Optimization meta-heuristic.In Corne D,Dorigo M and Glover F, New Ideas in Optimization.London:McGraw Hill,1999,11-32.
[58] M Dorigo, G Di Caro, LM Gambardella. Ant algorithms for discrete optimization. Artificial Life 1999,5(2):137–172.
[59] W J Gutjahr. A graph-based ant system and its convergence. Future Generat Comput Syst 2000,16(9):873–880.
[60] W J Gutjahr.A generalized convergence result for the graph based ant system.Technical Report99-09, Dept. of Statistics and Decision Support System.University of Vienna,Austria,1999.
[61] T Stützle, Dorigo M. A short convergence proof for a class of ACO algorithms. IEEE Trans Evolutionary Comput 2002;6(4):358–65.
[62] W J Gutjahr.ACO algorithms with guaranteed convergence to the optimal solution. Information Processing Letters.2002,82(3): 145-153.
[63] J H Yoo, R J La, A M Makowski.Convergence results for ant routing.Technical Reprot CSHCN 2003-46,Institute for Systems Research,University of Maryland,College Park(MD),2003.
[64] J H Yoo, R J La, A M Makowski.Convergence results for ant routing algorithms results for simple parallel network and perpectives.Technical Reprot CSHCN 2003-44,Institute for Systems Research,University of Maryland,College Park(MD),2003.
[65] A Badr, A Fahmy.A proof of convergence for ant algorithms.Intenational Journal of Intelligent Computing and Information, 2003.3(1):22-32.
[66] Y H Hou, Y W Wu, L J Lu. Generalized ant colony optimization for economic dispatch of power systems. Proceedings of the 2002 International Conference on Power System Technoligy, 2002, 1:225-229.
[67]段海滨,王道波.蚁群算法的全局收敛性研究及改进.系统工程与电子技术.2004, 26 (10): 1506-1509.
[68]冯远静,冯祖仁,彭勤科.一类自适应蚁群算法及其收敛性分析.控制理论与应用.2005, 22 (5): 713-717.
[69]石立宝,郝晋.随机摄动蚁群算法的收敛性及其数值特性分析.系统仿真学报.2004.16 (11): 2421-2424.
[70] M Zlochin, M Birattari, N Meuleau et al. Model-based search for combinatorial optimization: A critical survey. Ann Oper Res2004.131 (1–4):373–95.
[71] N Meuleau, M Dorigo. Ant colony optimization and stochastic gradient descent. Artificial Life 2002,8(2):103–21.
[72] C Blum.Theoretical and practical aspects of Ant colony optimization. Dissertations in Artificial Intelligence. Berlin: Akademische VerlagsgesellschaftAka GmbH; 2004.
[73] M Dorigo,L M Gambardella.Ant colonies for the traveling salesman problem.Biosystems.1997,(43): 73-81.
[74] V Maniezzo, A Colorni. The Ant System applied to the quadratic assignment problem. IEEE Trans Data Knowledge Engorged 1999.11(5):769–780.
[75] D Merkle, Middendorf, H Schmeck.Ant colony optimization for resource-constrained project scheduling. IEEE Trans Evolutionary Comput.2002.6(4):333–46.
[76] D Costa, A Hertz. Ants can color graphs. J Oper Res Soc 1997,9(8):295–305.
[77] LM Gambardella,éD Taillard, G Agazzi. MACS-VRPTW: A multiple ant colony system for vehicle routing problems with time windows.In: Corne D, Dorigo M, Glover F, editors. New ideas in optimization. London: McGraw-Hill; 1999.7(4): 63–76.
[78] S Alupoaei, S Katkoori. Ant colony system application to marcocell overlap removal. IEEE Trans Very Large Scale Integr (VLSI) Syst 2004,12(10):18–22.
[79] V Maniezzo, M Boschetti, M Jelasity. An ant approach to membership overlay design. In: Dorigo M, Birattari M, Blum C, Gambardella LM,Mondada F, Stützle T, editors. Proceedings of ANTS 2004—Fourth international workshop on Ant colony optimization and swarm intelligence.Lecture Notes in Comput Sci, vol. 3172. Berlin: Springer; 2004: 37–48.
[80] K Doerner, W J Gutjahr, R F Hartl et al. Pareto ant colony optimization: A metaheuristic approach to multiobjective portfolioselection. Ann Oper Res 2004,131:79–99.
[81] M Guntsch, M Middendorf. Pheromone modification strategies for ant algorithms applied to dynamic TSP. In: Boers EJW, Gottlieb J, Lanzi PL, Smith RE, Cagnoni S, Hart E, Raidl GR, Tijink H, editors. Applications of evolutionary computing: Proceedings of EvoWorkshops,2001. Lecture Notes in Comput Sci, vol. 2037. Berlin: Springer; 2001: 213–220.
[82] K Socha, M Sampels, M Manfrin. Ant algorithms for the university course timetabling problem with regard to the state-of-the-art. In:Cagnoni S, Romero Cardalda JJ, Corne DW, Gottlieb J, Guillot A, Hart E, Johnson CG, Marchiori E, Meyer A, Middendorf M, Raidl GR, editors.Applications of evolutionary computing, proceedings of EvoWorkshops 2003. Lecture Notes in Comput Sci, vol. 2611. Berlin: Springer;2003:334–345.
[83] X Gandibleux, X Delorme, V T’Kindt. An ant colony optimisation algorithm for the set packing problem. In: Dorigo M, Birattari M, Blum C,Gambardella LM, Mondada F, Stützle T, editors. Proceedings of ANTS 2004—Fourth international workshop on Ant colony optimization and swarm intelligence. Lecture Notes in Comput Sci, vol. 3172. Berlin: Springer; 2004:49–60.
[84] R Michel, M Middendorf. An island model based ant system with look ahead for the shortest supers equence problem. In: Eiben AE, B?ck T,SchoenauerM, Schwefel H-P, editors. Proceedings of PPSN-V, fifth international conference on parallel problem solving from nature. Lecture Notes in Comput Sci, vol. 1498. Berlin: Springer.1998:692–701.
[85] L M Gambardella, M Dorigo. Ant Colony System hybridized with a new local search for the sequential ordering problem. INFORMS J Comput2000,12(3):237–255.
[86] C Solnon. Ant can solve constraint satisfaction problems. IEEE Trans Evolutionary Comput 2002,6(4):347–357.
[87] R S Parpinelli, H S Lopes, A A Freitas. Data mining with an ant colony optimization algorithm. IEEE Trans Evolutionary Comput,2002,6(4):321–329.
[88] T N Bui, J R Rizzo.Finding maximum cliques with distributed ants. In: Deb K, et al., editors. Proceedings of the genetic and evolutionarycomputation conference (GECCO 2004). Lecture Notes in Comput Sci, vol. 3102. Berlin: Springer.2004:24–35.
[89] M Blesa, C Blum. Ant colony optimization for the maximum edge-disjoint paths problem. In: Raidl GR, Cagnoni S, Branke J, Corne DW,Drechsler R, Jin Y, Johnson CG, Machado P, Marchiori E, Rothlauf R, Smith GD, Squillero G, editors. Applications of evolutionary computing,proceedings of EvoWorkshops 2004. Lecture Notes in Comput Sci, vol. 3005. Berlin: Springer.2004:160–169.
[90] A Shmygelska, R Aguirre-Hernández, H H Hoos. An ant colony optimization algorithm for the 2D HP protein folding problem. In: Dorigo M,Di Caro G, Sampels M, editors. Ant algorithms—Proceedings of ANTS 2002—Third internationalworkshop. Lecture Notes in Comput Sci,vol. 2463. Berlin: Springe.2002:40–52.
[91] J D Moss, C G Johnson. An ant colony algorithm for multiple sequence alignment in bioinformatics. In: Pearson DW, Steele NC, Albrecht RF,editors. Artificial neural networks and genetic algorithms. Berlin: Springer.2003:182–186.
[92] J Bautista, J Pereira. Ant algorithms for assembly line balancing. In: Dorigo M, Di Caro G, Sampels M, editors. Ant algorithms—Proceedingsof ANTS 2002—Third international workshop. Lecture Notes in Comput Sci, vol. 2463. Berlin: Springer.2002: 65–75.
[93] L Bianchi, L M Gambardella, M Dorigo. An ant colony optimization approach to the probabilistic traveling salesman problem. In: Merelo JJ,Adamidis P, Beyer H-G, Fernández-Villacanas J-L, Schwefel H-P, editors. Proceedings of PPSN-VII, seventh international conference onparallel problem solving from nature. Lecture Notes in Comput Sci, vol. 2439. Berlin: Springer.2002:883–92.
[94]张高峰.梯级水电系统短期优化调度与自动发电控制研究:博士学位论文.华中科技大学图书馆,2004.
[95]陈森林.水库(群)调度基本理论及自动化系统研究:博士学位论文.武汉大学图书馆.2001.
[96]裴哲义,韩放.水电与电力市场.水电自动化与大坝监测.2002,26(1):24-31.
[97]曹生荣.基于知识工程和Agent理论的水库调度系统研究:博士学位论文.武汉大学图书馆.2004.
[98]刘攀.水库洪水资源化调度关键技术研究:博士学位论文.武汉大学图书馆.2004.
[99]郭生练.水库调度综合自动化系统,武汉:武汉水利电力大学出版社,2000.
[100]曾勇红,姜铁兵,张勇传.基于线性规划的梯级水电系统短期发电计划.水电自动化与大坝监测;2004,8:13-21.
[101]李建华.水电站短期优化调度研究与应用:硕士学位论文.武汉大学图书馆.2005.
[102] Mwangi Mbuthia, Hons,C.Eng. Hydrelectric system modeling for cascaded reservoir-type power stations in the Lower Tana River(seven forks scheme)in KENYA. AFRICON '92 Proceedings.3rd AFRICON Conference,1992,9(22-24):413– 416.
[103]张勇传著.水电能优化运行.湖北.华中工学院出版社.1986.6-7.
[104]刘鑫卿,张士军.梯级水电站流量滞后性影响研究.华中理工大学学报.1999,5 :74-81.
[105]胡浩.水资源价值与价格研究综述.石家庄经济学院学报,2003,26(3):229- 232.
[106]吴世勇,马光文,杨道辉.基于水值的水电商报价策略研究.水力发电.2006,32(7):74-76.
[107]吴世勇,徐温安,柳伟.在电力系统长期优化调度中的新概念—水值.水能技术经济, 1997,17(1): 108-110.
[108]黄席樾等.现代智能算法理论及应用.北京:科学出版社.2005.
[109] J L Deneubourg, J M Pasteels, J C Verhaeghe. Probabilistic behaviors in Ants: a Strategy of Errors. Journal of Theoretical Biology,1983,10(5):259-271.
[110] J L Deneubourg, S Goss. Collective patterns and decision-making. Ethology, Ecology&Evolution.1989,1:295-311.
[111] J L Deneubourg, S Aron, S Goss et al. The self-organizing exploratory pattern of the argentine ant. Journal of Insect Behaviour,1990,3:159-168.
[112] S Goss, S Aron, J L Deneubourg et al. Self-organized shortcuts in the Argentine ant. Naturwissenschaften,1989,76:579-581.
[113]陈森林.水电系统短期优化调度的一般性准则—基本概念与数学模型,武汉水力电力大学学报.1999,32(3):34-37.
[114] Jorge Valenzuela, Mainak Mazumdar. A Probability Model for the Electricity Price Duration Curve Under an Oligopoly Market.IEEE Trans.Power Syst.2005,20(8):1250-1256.
[115] B.Gjelsvik,A.Grundt,A.Karesen et al. Optimisation of hydropower operation in a liberalised market with focus on price modeling. Power Tech Proceedings, 2001 IEEE Porto Publication Date: 2001,10(9):654-668.
[116] Javier Contreras, Rosario Espínola, Francisco J. Nogales et al. ARIMA models to predict next-day electricity prices. IEEE Trans on Power System. 2003,18(3):1014-1021.
[117] J A Conejo, A P Miguel, Rosa Espínola et al. Day-ahead electricity price forecasting using the wavelet transform and ARIMA models. IEEE Trans on Power System. 2003,20(5):1035-1042.
[118]国家电力公司动力经济研究中心.水电上网电价研究.贵州水力发电.1999. 12:61-64.
[119]吴东平,杨晓谨.水电上网模式的讨论.水电自动化与大坝监测.2003, 23(6):7-8,16.
[120]顾伟忠,刘兰.我国水电上网电价的定价研究.北京机械工业学院学报,2004,19(2) :60-66.
[121]王黎,马光文.两部制上网电价定价方法初探.四川大学学报(工程科学版).2000,32(1):62-66.
[122]苏斌.采用“两部制”电价机制促进水电开发利用.中国物价.2004.04:5-10.
[123]江岳春,姚建刚,周丽兰等.基于“容量合同+效率置换+实时市场”模式的发电竞价系统的研究.电工技术学报.2006,21(1):52-57.
[124]包颖.浅议水电两部制上网电价.四川水利.2006.1:32-39.
[125]王锡凡,耿建.分段竞价与分时竞价的比较.电力系统自动化,2003,27(7):42-48.
[126]马光文,王黎,过夏明.分时电价下多用途水库的运用方式研究.水科学进展.2002, 13(5):36-41.
[127] L M Gambardella, M Dorigo. Solving symmetric and asymmetric TSPs by ant colonies. Evolutionary Computation, 1996, Proceedings of IEEE International Conference, 1996:622-627.
[128] M Dorigo, L M Gambardella. Ant colony system: a cooperative learning approach to the traveling salesman problem.IEEE Transactions on Evolutionary Computation, 1997, 1(1):53-66.
[129]詹士昌,徐婕,吴俊.蚁群算法中有关算法参数的最优选择.科技通报,2003,19(5):381-386.
[130]叶志伟,郑肇葆.蚁群算法中参数α、β、ρ设置的研究——以TSP问题为例.武汉大学学报·信息科学版,2004,29(7):597-601.
[131]华振兴.梯级水电站群短期优化调度:硕士学位论文,华中科技大学,2006.